glot_statusDAL tutorial - Week 5
Data transformation
1 Data transformation
Data transformation is a fundamental aspect of data analysis.
After the data you need to use is imported into R, you will have to filter rows, create new columns, or join data frames, among many other transformation operations.
In this tutorial we will learn how to filter() the data and mutate() or create new columns. In Week 6 (after Flexible Learning week) you will learn how to obtain summary measures and how to count occurrences using the summarise(), group_by() and count() functions.
2 Filter
Filtering data based on specific criteria couldn’t be easier with filter(), from the dplyr package (one of the tidyverse core packages),
Let’s work with the coretta2022/glot_status data frame. It’s an .rds file, so you need to use the readRDS() function. Go ahead and read the data into glot_status.
Before we can move on onto filtering data, we first need to learn about logical operators.
2.1 Logical operators
There are four main logical operators:
x == y:xequalsy.x != y:xis not equal toy.x > y:xis greater thany.x < y:xis smaller thany.
Logical operators return TRUE or FALSE depending on whether the statement they convey is true or false. Remember, TRUE and FALSE are logical values.
Try these out in the Console:
# This will return FALSE
1 == 2[1] FALSE
# FALSE
"apples" == "oranges"[1] FALSE
# TRUE
10 > 5[1] TRUE
# FALSE
10 > 15[1] FALSE
# TRUE
3 < 4[1] TRUE
Now let’s see how these work with filter()!
2.2 The filter() function
Filtering in R with the tidyverse is straightforward. You can use the filter() function.
filter() takes one or more statements with logical operators.
Let’s try this out. The following code filters the status column so that only the extinct status is included in the new data frame extinct.
You’ll notice we are using the pipe |> to transfer the data into the filter() function; the output of the filter function is assigned <- to extinct. The flow might seem a bit counter-intuitive but you will get used to think like this when writing R code soon enough!
extinct <- glot_status |>
filter(status == "extinct")
extinctNeat! What if we want to include all statuses except extinct? Easy, we use the non-equal operator !=.
not_extinct <- glot_status |>
filter(status != "extinct")
not_extinctAnd if we want only non-extinct languages from South America? We can include multiple statements separated by a comma!
south_america <- glot_status |>
filter(status != "extinct", Macroarea == "South America")
south_americaCombining statements like this will give you only those rows where both conditions apply. You can add as many statements as you need.
Now try to filter the data so that you include only not_endangered languages from all macro-areas except Eurasia. This time, don’t save the output to a new data frame. What happens? Where is the output shown?
glot_status |>
filter(...)This is all great, but what if we want to include more than one status or macro-area?
To do that we need another operator: %in%.
2.3 The %in% operator
Try these in the Console:
# TRUE
5 %in% c(1, 2, 5, 7)[1] TRUE
# FALSE
"apples" %in% c("oranges", "bananas")[1] FALSE
But %in% is even more powerful because the value on the left does not have to be a single value, but it can also be a vector! We say %in% is vectorised because it can work with vectors (most functions and operators in R are vectorised).
# TRUE, TRUE
c(1, 5) %in% c(4, 1, 7, 5, 8)[1] TRUE TRUE
stocked <- c("durian", "bananas", "grapes")
needed <- c("durian", "apples")
# TRUE, FALSE
needed %in% stocked[1] TRUE FALSE
Try to understand what is going on in the code above before moving on.
2.4 Now filter the data
Now we can filter glot_status to include only the macro-areas of the Global South and only languages that are either “threatened”, “shifting”, “moribund” or “nearly_extinct”. I have started the code for you, you just need to write the line for filtering status.
global_south <- glot_status %>%
filter(
Macroarea %in% c("Africa", "Australia", "Papunesia", "South America"),
...
)This should not look too alien! The first statement, consonant %in% c("p", "t", "k") looks at the consonant column and, for each row, it returns TRUE if the current row value is in c("p", "t", "k"), and FALSE if not.